Environmental indicator analysis-Tilefish
# Loading packages and data
library(ggplot2)
library(ecodata)
library(lubridate)
library(dplyr)
library(stringr)
library(marmap) # bathymetry
library(RColorBrewer)
library(ggnewscale)
library(sf)
library(cowplot)
library(tidyverse)
library(ggpubr)
library(sf)
library(ggdist)
library(ggpubr)
library(wesanderson)
library(raster)
#library(glmTMB)
library(ggpmisc)
library(mgcViz)
library(gratia)
# CPUE data (no env covariates)
gt_data_model_cpue <- read.csv(here::here('data/catch_data/gt_data_model_cpue.csv'))
#names(gt_data_model_cpue) <- tolower(names(gt_data_model_cpue))
# Add in column with cpue
# note: Paul indicated to use small mesh
gt_data_model_cpue <- gt_data_model_cpue %>%
rename_all(., .funs = tolower) %>%
mutate(mesh_bin = case_when(mesh_size <= 5.6 ~ 'SM',mesh_size >= 5.6 ~ 'LG',
TRUE ~ 'NA')) %>%
mutate(cpue_hr = sum_gt_catch/effort_dur)
# Catch data:
sfobs <-readRDS(here::here('data/catch_data/gold_tile_sf_ob_v1_temp_price.rds'))
sfob.env <- sfobs %>%
mutate(mesh_bin = case_when(mesh_size <= 5.6 ~ 'SM', mesh_size >= 5.6 ~ 'LG',
TRUE ~ 'NA'),
cpue_hr = SUM_GT_CATCH/effort_dur) %>%
filter(YEAR %in% c(1998:2022) & mesh_bin == 'SM') %>%
dplyr::select(DATE, YEAR, MONTH, YDAY,trip_id,hull_num, area, effort_dur,
SUM_GT_CATCH, cpue_hr, mesh_size, mesh_bin, depth, start_lat, start_lon,
bottomT, bottomT_avg, MIN_TEMP_C, MEAN_TEMP_C, MAX_TEMP_C,
TEMP_VARIANCE, TEMP_DEVIATION, MEAN_DPTH_M, tri, sed) %>%
mutate(YEAR = as.integer(YEAR)) %>%
rename_all(., .funs = tolower)
areas <- sort(unique(sfob.env$area))
catch.tally.ann <- sfob.env %>% # aggregate by year
group_by(year) %>%
summarise(ttl_sum = sum(sum_gt_catch))
# Length data from observer program
lengths <- read.csv(here::here('data/catch_data/gt_data_length_andy.csv'))
names(lengths) <- tolower(names(lengths))
# Recruitment estimates from 2021 report
recruit <- read.csv(here::here('data/assessment_data/tilefish_rec_estimate_2021.csv'))
# Merge SF/Obs catch data with recruit estimates:
catch_recruit <- cbind(recruit %>% filter(year %in% c(1998:2020)),
catch.tally.ann %>%
filter(year %in% c(1998:2020)) %>%
dplyr::select(ttl_sum))
# loading in shape files for maps
US.areas <- st_read(here::here('shapefiles/USA.shp'), quiet = TRUE)
canada.areas <- st_read(here::here('shapefiles/Canada.shp'), quiet = TRUE)
bts_strata <- st_read(here::here('shapefiles/NES_BOTTOM_TRAWL_STRATA.shp'),
quiet = TRUE)
# plot(bts_strata) # to see all bottom trawl strata
gtf_strata <- bts_strata %>%
filter(STRATUMA %in% c('01030', '01040', '01070', '01080', '01110', '01120',
'01140', '01150', '01670', '01680', '01710', '01720',
'01750', '01760')) # select just the gtf strata
# plot(gtf_strata)
bathy <- marmap::getNOAA.bathy(-81,-58, 27, 46)
bathy = fortify.bathy(bathy)Tilefish data
Catch data
Year-class strength is broadly defined as the number of fish spawned or hatched in a given year (Ricker, 1975).
Figure 1. Sum of catch (not accounting for effort), across years. Light blue shaded region represents the temporal range of observer records and red shaded region represents temporal range of study fleet records. The ‘purple’ region is where they overlap. Note that 2000-2005 for observer records had low sample size/number of vessels for tilefish, making the shaded region likely the best region to use for analysis. The vertical dashed lines represent strong year classes for this species (Nesslage et al. 2021). Red asterisk marks year that stock was deemed ‘re-built’.
# tot_catch == total (sum_catch) across hauls. so if tallying up annually,
# use sum_catch
# Strong year-classes: 1970, 1973, 1993, 1999, 2005, 2013
ggplot(catch.tally.ann, aes(x = factor(year), y = ttl_sum, group = 1))+
geom_rect(aes(xmin = '2007', xmax = '2022', ymin = -Inf, ymax = Inf),
fill = 'red', alpha = 0.02) +
geom_rect(aes(xmin = '2000', xmax = '2022', ymin = -Inf, ymax = Inf),
fill = 'lightblue', alpha = 0.05) +
geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
geom_line(color = 'black', size = 1.5) +
annotate("text", label = "*",
x = 26, y = 14000, size = 8, colour = "red" )+
xlab('Year') +
ylab('Total sum tilefish catch') +
# facet_wrap(~month)+
theme(axis.text.x = element_text(color = 'black',
size = 12, angle = 45, vjust = 1, hjust=1)) +
ecodata::theme_facet()
CPUE
Figure 2. Catch-per-unit-effot for undirected trawl trips from the Study fleet and observer program. Zeros have been added using species association methodology (via jaccard index).
gt_data_model_cpue %>%
filter(mesh_bin == 'SM') %>% # note: Paul indicated to use small mesh
group_by(year, source) %>%
summarise(mean_cpue = mean(cpue_hr),.groups = 'drop') %>%
ggplot(aes(x=year,y=mean_cpue)) +
geom_line(lwd = 1) +
facet_wrap(~source) +
theme_bw()
gt_data_model_cpue %>%
filter(mesh_bin == 'SM') %>%
group_by(year) %>%
summarise(mean_cpue = mean(cpue_hr),.groups = 'drop') %>%
ggplot(aes(x=year,y=mean_cpue)) +
geom_line(lwd = 1) +
labs(title = 'Study fleet + Observer combined') +
theme_bw()
Maps (catch)
Tilefish catch locations (study fleet/observer)
yrs = sort(unique(gt_data_model_cpue$year))
#for(i in 1:length(yrs)){
yrmap <- function(yrs){
gt_data_model_cpue %>%
filter(start_lat < 42.5 & depth_est > 50 & year == yrs) %>%
mutate(bin = cut(year, seq(min(year), max(year) + 4, 4), right = FALSE)) %>%
ggplot() +
geom_sf(data = US.areas %>% st_as_sf(),color = 'gray20', fill = '#cbdbcd') +
geom_contour(data = bathy,
aes(x=x,y=y,z=-1*z),
breaks=c(50,100,150,200, Inf),
size=c(0.3),
col = 'darkgrey') +
stat_summary_2d(aes(x=start_lon, y=start_lat, z = cpue_hr),
binwidth=c(0.16666,0.16666)) +
scale_fill_viridis_c() +
theme(legend.position = "bottom",
legend.key.size = unit(0.2, "cm"),
legend.key.width = unit(1, "cm")) +
coord_sf(xlim = c(-75,-65.5), ylim = c(36,44), datum = sf::st_crs(4326)) +
labs(x = '', y = '', fill = 'CPUE') +
theme_bw()
}
for(i in 1:length(yrs)){
cat("\n#####", as.character(yrs[i]),"\n")
print(yrmap(yrs[i]))
cat("\n")
}2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
Recruitment estimate
Figure 3. Age-1 recruitment estimate from the 2021 tilefish assessment across all years
ggplot(recruit, aes(x = factor(year), y = recruit_est, group = 1))+
geom_rect(aes(xmin = '2007', xmax = '2022', ymin = -Inf, ymax = Inf),
fill = 'red', alpha = 0.02) +
geom_rect(aes(xmin = '2000', xmax = '2022', ymin = -Inf, ymax = Inf),
fill = 'lightblue', alpha = 0.05) +
geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
geom_line(color = 'black', size = 1.5) +
annotate("text", label = "*",
x = 26, y = 14000, size = 8, colour = "red" )+
xlab('Year') +
ylab('Total sum tilefish catch') +
# facet_wrap(~month)+
theme(axis.text.x = element_text(color = 'black',
size = 12, angle = 45, vjust = 1, hjust=1)) +
ecodata::theme_facet()
### Thought: Should we isolate years associated w/strong year classes (or bad)
### for correlations and analyses? Figure 4. Recruitment estimates in focus years
- Paul has suggested that data used to calculate recruitment estimates was strongest from 2000 to present.
- Extended to 1998 to capture both the strong year class and El nino associated with that year.
ggplot(recruit %>% filter(year %in% c(1998:2022)),
aes(x = factor(year), y = recruit_est, group = 1))+
geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
geom_line(color = 'black', size = 1.2) +
xlab('Year') +
ylab('Recruit estimates') +
theme(axis.text.x = element_text(color = 'black',
size = 12, angle = 45, vjust = 1, hjust=1)) +
ecodata::theme_facet()
Figure 5. Recruitment estimates and Study Fleet and Observer catch data. Black line denotes recruitment estimate, yellow denotes sum of annual catch data across both Study fleet and Observer programs.
options(scipen=999)
ggplot(catch_recruit) +
geom_line(aes(x = factor(year), y = recruit_est, group = 1),
col = 'black', size = 1.2) +
geom_line(aes(x = factor(year), y = ttl_sum*1000), size = 1.2,
color = 'goldenrod1', group = 1) +
scale_y_continuous(sec.axis = sec_axis(~./1000, name = 'Catch (lbs)')) +
geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
xlab('Year') +
ylab('Recruit estimates') +
theme(axis.text.x = element_text(color = 'black',
size = 12, angle = 45, vjust = 1, hjust=1)) +
ecodata::theme_facet()
Length data
All lengths
Figure 6. Distribution of lengths Figure 7. Length frequencies Figure 8. Frequency of smaller individuals
# Define category breaks
size_breaks <- c(0,10,20,30,40, 50, 60, 70, 80, 90, 100)
# Making a function to bin the catches
label_interval <- function(breaks) {
paste0("(", breaks[1:length(breaks) - 1], "-", breaks[2:length(breaks)], ")")
}
labels = label_interval(size_breaks)
# length freq. table
tab = table(cut(lengths$lenanml,
breaks = size_breaks,
labels = label_interval(size_breaks)))
## Plot full distribution
ggplot(lengths,
aes(x = lenanml)) +
geom_bar(position = position_dodge(),
alpha = 0.4, fill= 'blue', color="black") +
xlab('Tilefish length (cm)') +
theme_bw() +
theme_facet()
# Plot length frequencies
barplot(tab, xlab = 'Length bins (mm)', main = '')
# Just the little ones
barplot(tab[1:3], xlab = 'Length bins (mm)', main = '')
Juveniles
Young of year - year 1 and 2 size class
ggplot(lengths %>% filter(lenanml <= 26),
aes(x = lenanml)) +
geom_bar(position = position_dodge(),
fill= 'slateblue', color="black") +
xlab('Tilefish length (cm)') +
theme_bw() +
theme_facet()
ggplot(lengths %>% filter(lenanml <= 26),
aes(x = lenanml, fill = numlen)) +
geom_bar(position = position_dodge(),
alpha = 0.4, fill= 'blue', color="black") +
xlab('Tilefish length (cm)') +
theme_bw() +
facet_wrap(~year) +
theme_facet()
Environmental data
The strong year classes for Golden Tilefish were 1993, 1998, 2005, 2013. Some of the underlying oceanographic processes that may be related to recruitment may influence habitat, retention/displacement and food availablity. These are explored below.
Habitat
Tilefish occupy a very narrow band of habitat conditions. Therefore, temperature and salinity may be of interest.
SST
# SST
sst<-read.csv(here::here('data/sst/sst_ts_gtf_strata.csv'))Maps (SST)
SST analysis
# SST with year lag
sst.lag <- sst %>%
mutate(mean_sst_lag1 = lag(weighted_mean_sst,12))
# Join with recruit estimate
df <- dplyr::full_join(recruit, sst.lag %>%
group_by(year) %>%
filter(year %in% c(2000:2020)),
dplyr::select(year, month, mean_cpue, mean_sst, weighted_mean_sst, mean_sst_lag1),
by = join_by(year))
sst.rec <- df[-c(1:29),] #removes year < 2000 (when SST data begins)
#by season
sst_winter <- filter(sst.rec, month %in% c(1,2,3))
sst_spring <- filter(sst.rec, month %in% c(4,5,6))
sst_summer <- filter(sst.rec, month %in% c(7,8,9))
sst_fall <- filter(sst.rec, month %in% c(10,11,12))
## Winter
lm_winter<-lm(weighted_mean_sst ~ recruit_est, data=sst_winter)
summary(lm_winter)##
## Call:
## lm(formula = weighted_mean_sst ~ recruit_est, data = sst_winter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4490 -1.1651 0.0387 0.8677 3.8249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6202895083 0.4455096534 19.349 <0.0000000000000002 ***
## recruit_est 0.0000001720 0.0000002991 0.575 0.568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.657 on 60 degrees of freedom
## Multiple R-squared: 0.005477, Adjusted R-squared: -0.0111
## F-statistic: 0.3304 on 1 and 60 DF, p-value: 0.5676cor.test(sst_winter$recruit_est, sst_winter$weighted_mean_sst, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: sst_winter$recruit_est and sst_winter$weighted_mean_sst
## t = 0.57483, df = 60, p-value = 0.5676
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1790712 0.3178990
## sample estimates:
## cor
## 0.07400706ggscatter(sst_winter, x = "recruit_est", y = "weighted_mean_sst",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Winter Weighted Mean SST",
title="Winter")
## Spring
lm_spring<-lm(weighted_mean_sst ~ recruit_est, data=sst_spring)
summary(lm_spring)##
## Call:
## lm(formula = weighted_mean_sst ~ recruit_est, data = sst_spring)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1147 -3.2854 -0.5526 3.7471 7.0917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.1587863384 1.0115356419 12.020 <0.0000000000000002 ***
## recruit_est 0.0000003556 0.0000006644 0.535 0.594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.828 on 61 degrees of freedom
## Multiple R-squared: 0.004674, Adjusted R-squared: -0.01164
## F-statistic: 0.2865 on 1 and 61 DF, p-value: 0.5944cor.test(sst_spring$recruit_est, sst_spring$weighted_mean_sst, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: sst_spring$recruit_est and sst_spring$weighted_mean_sst
## t = 0.53523, df = 61, p-value = 0.5944
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1824868 0.3108685
## sample estimates:
## cor
## 0.06836944ggscatter(sst_spring, x = "recruit_est", y = "weighted_mean_sst",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Spring Weighted Mean SST",
title="Spring")
## Summer
lm_summer<-lm(weighted_mean_sst ~ recruit_est, data=sst_summer)
summary(lm_summer)##
## Call:
## lm(formula = weighted_mean_sst ~ recruit_est, data = sst_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.51168 -0.79996 0.01489 0.83898 2.73702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.8009040387 0.3071009301 74.25 <0.0000000000000002 ***
## recruit_est 0.0000000585 0.0000002017 0.29 0.773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.162 on 61 degrees of freedom
## Multiple R-squared: 0.001377, Adjusted R-squared: -0.01499
## F-statistic: 0.08411 on 1 and 61 DF, p-value: 0.7728cor.test(sst_summer$recruit_est, sst_summer$weighted_mean_sst, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: sst_summer$recruit_est and sst_summer$weighted_mean_sst
## t = 0.29003, df = 61, p-value = 0.7728
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2126115 0.2822781
## sample estimates:
## cor
## 0.03710834ggscatter(sst_summer, x = "recruit_est", y = "weighted_mean_sst",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean SST",
title="Summer")
## Fall
lm_fall<-lm(weighted_mean_sst ~ recruit_est, data=sst_fall)
summary(lm_fall)##
## Call:
## lm(formula = weighted_mean_sst ~ recruit_est, data = sst_fall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2757 -2.4013 -0.3087 2.5850 5.1703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.1735575824 0.8010338367 20.191 <0.0000000000000002 ***
## recruit_est -0.0000002495 0.0000005262 -0.474 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.032 on 61 degrees of freedom
## Multiple R-squared: 0.003674, Adjusted R-squared: -0.01266
## F-statistic: 0.2249 on 1 and 61 DF, p-value: 0.637cor.test(sst_fall$recruit_est, sst_fall$weighted_mean_sst, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: sst_fall$recruit_est and sst_fall$weighted_mean_sst
## t = -0.47428, df = 61, p-value = 0.637
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3038162 0.1900047
## sample estimates:
## cor
## -0.06061386ggscatter(sst_fall, x = "recruit_est", y = "weighted_mean_sst",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Fall Weighted Mean SST",
title="Fall")
## One year lag - Summer (peak spawn)
lm_lag<-lm(mean_sst_lag1 ~ recruit_est, data=sst_summer)
summary(lm_lag)##
## Call:
## lm(formula = mean_sst_lag1 ~ recruit_est, data = sst_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.38712 -0.80602 0.09212 0.85112 2.75231
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.9503271664 0.3300939770 69.53 <0.0000000000000002 ***
## recruit_est -0.0000000676 0.0000002329 -0.29 0.773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.173 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.001451, Adjusted R-squared: -0.01577
## F-statistic: 0.08425 on 1 and 58 DF, p-value: 0.7727cor.test(sst_summer$recruit_est, sst_summer$mean_sst_lag1, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: sst_summer$recruit_est and sst_summer$mean_sst_lag1
## t = -0.29026, df = 58, p-value = 0.7727
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2892133 0.2179469
## sample estimates:
## cor
## -0.03808556ggscatter(sst_summer, x = "recruit_est", y = "mean_sst_lag1",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean SST",
title="One Year Lag")
Bottom Temperature
Figure 1. GLORYS vs in-situ bottom temperatures from study fleet vessels.
Figure 2. Bottom temperature (C) across years. Blue dots are in-situ data, red dots are from GLORYS.
ggplot2::ggplot(sfob.env, aes(x=bottomt, y=mean_temp_c)) +
geom_point(color="blue", alpha=0.1)+
geom_abline(intercept = 0, slope = 1) +
xlab('Bottom Temp (SF)') +
ylab('Bottom Temp (GLORYS)') +
theme_bw() 
ggplot2::ggplot(sfob.env, aes(x=bottomt, y=year)) +
geom_point(color="blue", alpha=0.1) +
geom_point(data = sfob.env, aes(x=mean_temp_c, y=year),
color="red", alpha=0.1) +
xlab('Bottom Temp') +
ylab('Year') +
labs(color = 'Source') +
theme_bw() 
Maps (Bottom T)
jet.colors <-colorRampPalette(c("blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
# select just years with study fleet bottom temps
sf.bt <- sfob.env %>% filter(year>2006 & depth > 50)
yrs = sort(unique(sf.bt$year))
#for(i in 1:length(yrs)){
yrmap <- function(yrs){
sf.bt %>% filter(year == yrs) %>%
ggplot() +
geom_sf(data = US.areas %>% st_as_sf(),color = 'gray20', fill = '#cbdbcd') +
geom_contour(data = bathy,
aes(x=x,y=y,z=-1*z),
breaks=c(50,100,150,200, Inf),
size=c(0.3),
col = 'darkgrey') +
stat_summary_2d(aes(x=start_lon, y=start_lat, z = bottomt),
binwidth=c(0.16666,0.16666)) +
scale_fill_gradientn(colors = jet.colors(20)) +
coord_sf(xlim = c(-75,-65.5), ylim = c(36,44), datum = sf::st_crs(4326)) +
labs(x = '', y = '', fill = 'Bottom temperature (°C)') +
theme_bw()
}
for(i in 1:length(yrs)){
cat("\n######", as.character(yrs[i]),"\n")
print(yrmap(yrs[i]))
cat("\n")
}2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
Bottom T analysis
The following figures compare in-situ bottom temperature from the study-fleet data set to the recruitment estimate.
- Note here temperatures are averaged across all depths > 50 for each month.
# Note here temperatures are averaged across all depths > 50 for each month.
# Create in-situ bottom temps by month w/lag
df.lag = sfob.env %>% filter(year > 2006 & depth > 50) %>%
group_by(year,month) %>%
summarise(mean_dpth = mean(depth),
mean_bt = mean(bottomt)) %>%
mutate(mean_bt_lag2 = lag(mean_bt,2),
mean_bt_lag3 = lag(mean_bt,3),
mean_bt_lag6 = lag(mean_bt, 6))
# Join in-situ bottom temps w/assessment recruitment estimate
df.join.bt = dplyr::full_join(recruit, df.lag, by = join_by(year)) %>%
dplyr::select(year, month, recruit_est, mean_dpth,
mean_bt, mean_bt_lag2, mean_bt_lag3, mean_bt_lag6) %>%
tidyr::drop_na()
# See what months have data
sort(unique(df.join.bt$month))## [1] 7 8 9 10 11 12hist(df.join.bt$month) # will group into spring/summer fall/winter categories
## spring/summer bottom temp no lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(4,5,6,7,8)),
aes(x=recruit_est, y=mean_bt)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Spring/Summer') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## fall/winter bottom temp no lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(9,10,11,12)),
aes(x=recruit_est, y=mean_bt)) +
geom_point(color= 'black')+
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Fall/Winter') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## spring/summer bottom temp 2 month lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(4,5,6,7,8)),
aes(x = recruit_est, y = mean_bt_lag2)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Lag 2 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## fall/winter bottom temp 2 month lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(9,10,11,12)),
aes(x = recruit_est, y = mean_bt_lag2)) +
geom_point(color= 'black')+
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Lag 2 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## spring/summer bottom temp 3 month lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(4,5,6,7,8)),
aes(x = recruit_est, y = mean_bt_lag3)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Lag 3 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## fall/winter bottom temp 3 month lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(9,10,11,12)),
aes(x = recruit_est, y = mean_bt_lag3)) +
geom_point(color= 'black')+
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Lag 3 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## spring/summer bottom temp 6 month lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(4,5,6,7,8)),
aes(x = recruit_est, y = mean_bt_lag6)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Lag 6 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## fall/winter bottom temp 6 month lag
ggplot2::ggplot(df.join.bt %>% filter(month %in% c(9,10,11,12)),
aes(x = recruit_est, y = mean_bt_lag6)) +
geom_point(color= 'black')+
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean bottom temp (°C)')+
labs(title = 'Lag 6 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
Correlations
lm_bt<-lm(mean_bt ~ recruit_est, data=df.join.bt)
summary(lm_bt)##
## Call:
## lm(formula = mean_bt ~ recruit_est, data = df.join.bt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3652 -0.7175 -0.0739 1.5142 2.9461
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.8551500741 1.4502394850 8.175 0.00000532 ***
## recruit_est 0.0000001517 0.0000009295 0.163 0.873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.139 on 11 degrees of freedom
## Multiple R-squared: 0.002417, Adjusted R-squared: -0.08827
## F-statistic: 0.02665 on 1 and 11 DF, p-value: 0.8733cor.test(df.join.bt$recruit_est, df.join.bt$mean_bt, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: df.join.bt$recruit_est and df.join.bt$mean_bt
## t = 0.16325, df = 11, p-value = 0.8733
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5157938 0.5843205
## sample estimates:
## cor
## 0.04916354ggscatter(df.join.bt, x = "recruit_est", y = "mean_bt",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean bottom temp (°C)")
#t_gam <- gam(recruit_est ~ s(mean_bt), data=df.join.bt, method="REML")
#draw(bt_gam, residuals=TRUE)Salinity
Here we explore salinity from the GLORYS reanalysis model at three different depths
- 55 meters (relevant to larvae, recruits)
- 110 meters (relevant recruits, juveniles)
- 220 meters (relevant to juveniles, adults)
# Salinity Maps (Salinity)
jet.colors <-colorRampPalette(c("blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
# b <- brick(here::here('data/salinity/dd_sal_55_2000_2009.tif'))
# b.00 <- b[,,,1:365]
# select just years with study fleet bottom temps
sf.bt <- sfob.env %>% filter(year>2006 & depth > 50)
yrs = sort(unique(sf.bt$year))
#for(i in 1:length(yrs)){
yrmap <- function(yrs){
sf.bt %>% filter(year == yrs) %>%
ggplot() +
geom_sf(data = US.areas %>% st_as_sf(),color = 'gray20', fill = '#cbdbcd') +
geom_contour(data = bathy,
aes(x=x,y=y,z=-1*z),
breaks=c(50,100,150,200, Inf),
size=c(0.3),
col = 'darkgrey') +
# stat_summary_2d(aes(x=start_lon, y=start_lat, z = bottomt),
# binwidth=c(0.16666,0.16666)) +
# scale_fill_gradientn(colors = jet.colors(20)) +
coord_sf(xlim = c(-75,-65.5), ylim = c(36,44), datum = sf::st_crs(4326)) +
labs(x = '', y = '', fill = 'Salinty') +
theme_bw()
}
for(i in 1:length(yrs)){
cat("\n######", as.character(yrs[i]),"\n")
print(yrmap(yrs[i]))
cat("\n")
}2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
Salinity analysis
Currents
Cross-shelf processes may influence the retention or displacement of tilefish during early life history stages. These are explored below.
Shelf water volume
Shelf water volume: A measure of the volume of water bounded inshore of the shelf-slope front. In this analysis, shelf water is defined as all water having salinity <34 psu. The position of the shelf-slope front varies inter-annually with the higher shelf water values indicating the front being pushed further towards the shelf break.
high shv: front pushed towards sbf low shv: front pushed inshore (more slope water on shelf)
Hypothesis: Higher recruitment success correlated with years of higher shelf water volume in spring/summer. These months months may be particularly important as that is when spawning is occurring and the position of the sbf may influence the position of larvae (away from spawning grounds).
Additional variables in this dataset are shelf water temperature and salinity which may also be indicative of habitat conditions.
# Shelf water volume
shlfvol <- read.csv(here::here('data/shelf_water_volume/ShelfWaterVolume_BSB_update.csv'))
# wrangling date info, converting doy to date and month
yrs <- as.vector(nrow(shlfvol))
shlfvol$Year <- as.character(shlfvol$Year)
for (i in 1:nrow(shlfvol)){
yrs[i] <- strsplit(shlfvol$Year, ".", fixed = TRUE)[[i]][1]
}
shlfvol$year <- yrs
shlfvol <- shlfvol %>% mutate(date_= as.Date(Year.Day-1,
origin=paste0(year, "-01-01")),
month= strftime(date_, "%m"),
day=strftime(date_,"%d"),
year = as.integer(year),
month = as.numeric(month))
# Create shw vol by month w/lag
df.lag = shlfvol %>%
group_by(year,month) %>%
summarise(mean_t = mean(ShW.T),
mean_s = mean(ShW.S),
mean_v = mean(ShW.Vol)) %>%
mutate(mean_t_lag2 = lag(mean_t,2),
mean_t_lag3 = lag(mean_t,3),
mean_t_lag6 = lag(mean_t,6),
mean_s_lag2 = lag(mean_s,2),
mean_s_lag3 = lag(mean_s,3),
mean_s_lag6 = lag(mean_s,6),
mean_v_lag2 = lag(mean_v,2),
mean_v_lag3 = lag(mean_v,3),
mean_v_lag6 = lag(mean_v,6))
# Join in-situ bottom temps w/assessment recruitment estimate
df.join.shlf = dplyr::full_join(recruit, df.lag, by = join_by(year)) %>%
dplyr::select(year, month, recruit_est, mean_t, mean_s, mean_v,
mean_t_lag2, mean_s_lag2, mean_v_lag2,
mean_t_lag3, mean_s_lag3, mean_v_lag3) %>%
tidyr::drop_na()
# See what months have data
sort(unique(df.join.shlf$month))## [1] 7 8 9 10 11hist(df.join.shlf$month) # will group into spring/summer fall/winter categories
## Shelf water volume no lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_v)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water volume') +
labs(title = 'Shelf water volume') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water temperature no lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_t)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water temperature') +
labs(title = 'Shelf water temperature') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water salinity no lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_s)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water salinity') +
labs(title = 'Shelf water salinity') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
ggplot2::ggplot(df.join.shlf %>% filter(year >1997 & month %in% c(7,8,9)),
aes(x=recruit_est, y=mean_s)) +
geom_point() +
geom_smooth(method = "lm", formula = y ~ x, size = 1, se = FALSE,
aes(colour = 'Linear')) +
geom_smooth(method = "lm", formula = y ~ x + I(x^2),
size = 1, se = FALSE, aes(colour = 'Quadratic')) +
geom_smooth(method = "loess", formula = y ~ x,
size = 1, se = FALSE, aes(colour = 'Loess')) +
geom_smooth(method = "gam", formula = y ~ s(x),
size = 1, se = FALSE, aes(colour = 'Gam')) +
geom_smooth(method = "gam", formula = y ~ s(x, k = 3),
size = 1, se = FALSE, aes(colour = 'Gam2')) +
labs(title = 'Recruitment est x M.S.W July,Aug,Sept (1998:2020)') +
scale_color_manual(name='Model',
breaks=c('Linear', 'Quadratic', 'Loess', 'Gam', 'Gam2'),
values=c('Linear'='black', 'Quadratic'='blue',
'Loess'='red', 'Gam' = 'green',
'Gam2' = 'purple')) +
theme_bw()
With lags
2 Months
## Shelf water volume 2 month lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_v_lag2)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water volume') +
labs(title = 'Shelf water volume - lag 2 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water temperature 2 month lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_t_lag2)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water temperature') +
labs(title = 'Shelf water temperature - lag 2 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water salinity 2 month lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_s_lag2)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water salinity') +
labs(title = 'Shelf water salinity - lag 2 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
3 Months
## Shelf water volume 3 month lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_v_lag3)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water volume') +
labs(title = 'Shelf water volume - lag 3 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water temperature 3 month lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_t_lag3)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water temperature') +
labs(title = 'Shelf water temperature - lag 3 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water salinity 3 month lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_s_lag3)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water salinity') +
labs(title = 'Shelf water salinity - lag 3 months') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
Annual
# Create shw vol by year w/lag
df.lag = shlfvol %>%
group_by(year, month) %>%
summarise(mean_t = mean(ShW.T),
mean_s = mean(ShW.S),
mean_v = mean(ShW.Vol)) %>%
mutate(mean_t_lag2 = lag(mean_t,2),
mean_t_lag3 = lag(mean_t,3),
mean_t_lag6 = lag(mean_t,6),
mean_s_lag2 = lag(mean_s,2),
mean_s_lag3 = lag(mean_s,3),
mean_s_lag6 = lag(mean_s,6),
mean_v_lag2 = lag(mean_v,2),
mean_v_lag3 = lag(mean_v,3),
mean_v_lag6 = lag(mean_v,6))
# Join in-situ bottom temps w/assessment recruitment estimate
df.join.shlf = dplyr::full_join(recruit, df.lag, by = join_by(year)) %>%
dplyr::select(year, month, recruit_est, mean_t, mean_s, mean_v,
mean_t_lag2, mean_s_lag2, mean_v_lag2,
mean_t_lag3, mean_s_lag3, mean_v_lag3,
mean_t_lag6, mean_s_lag6, mean_v_lag6) %>%
tidyr::drop_na()
## Shelf water vol
ggplot2::ggplot(df.join.shlf,
aes(x=year, y=mean_v)) +
geom_point(color = 'black') +
geom_line(color = 'black') +
xlab('Year') +
ylab('Mean shelf water volume') +
labs(title = 'Shelf water volume') +
theme_bw()
ggplot2::ggplot() +
geom_line(data = df.join.shlf, aes(x=year, y=mean_s), color = 'red') +
geom_line(data = df.join.shlf,aes(x=year, y=mean_t*1), color = 'blue') +
ylim(30.0,34.0) +
scale_y_continuous(name = 'Sh.Water Salinity',
sec.axis = sec_axis(~./1, name = 'Sh.Water Temperature')) +
xlab('Year') +
labs(title = 'Shelf water salinity/temperature') +
theme_bw()
ggplot2::ggplot() +
geom_line(data = df.join.shlf, aes(x=year, y=mean_s), color = 'red') +
xlab('Year') +
ylab('Mean shelf water salinity') +
labs(title = 'Shelf water salinity') +
theme_bw()
## Shelf water vol no lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_v)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water volume') +
labs(title = 'Shelf water volume') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water temperature no lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_t)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water temperature') +
labs(title = 'Shelf water temperature') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water salinity no lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_s)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water salinity') +
labs(title = 'Shelf water salinity') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
With lags
## Shelf water vol 6 yr lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_v_lag6)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water volume') +
labs(title = 'Shelf water volume - lag 6 years') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water temperature 6 yr lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_t_lag6)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water temperature') +
labs(title = 'Shelf water temperature - lag 6 years') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
## Shelf water salinity 6 yr lag
ggplot2::ggplot(df.join.shlf,
aes(x=recruit_est, y=mean_s_lag6)) +
geom_point(color = 'black') +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth") +
xlab('Recruitment estimate') +
ylab('Mean shelf water salinity') +
labs(title = 'Shelf water salinity - lag 6 years') +
stat_cor(aes(label=..rr.label..)) +
theme_bw()
print(paste0('correlation: ', round(cor(df.join.shlf[,2], df.join.shlf[,13]), 4)))## [1] "correlation: NA"Correlations
df.join.shlf = dplyr::full_join(recruit, df.lag, by = join_by(year)) %>%
dplyr::select(year, month, recruit_est, mean_t, mean_s, mean_v) %>%
tidyr::drop_na() #removing lags to visualize all months
df.join.shlf = mutate(df.join.shlf, mean_v_lag1 = lag(mean_v,12)) #creating one year lag
#by season
shlfvol_winter <- filter(df.join.shlf, month %in% c(1,2,3))
shlfvol_spring <- filter(df.join.shlf, month %in% c(4,5,6))
shlfvol_summer <- filter(df.join.shlf, month %in% c(7,8,9))
shlfvol_fall <- filter(df.join.shlf, month %in% c(10,11,12))
## shelf water volume - one year lag (summer = peak spawn)
lm_lag<-lm(mean_v_lag1 ~ recruit_est, data=shlfvol_summer)
summary(lm_lag)##
## Call:
## lm(formula = mean_v_lag1 ~ recruit_est, data = shlfvol_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1459.95 -542.14 -91.96 353.47 1515.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4035.16932167 221.39861347 18.226 <0.0000000000000002 ***
## recruit_est 0.00005873 0.00013827 0.425 0.673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 745.8 on 53 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.003392, Adjusted R-squared: -0.01541
## F-statistic: 0.1804 on 1 and 53 DF, p-value: 0.6727cor.test(shlfvol_summer$recruit_est, shlfvol_summer$mean_v_lag1, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: shlfvol_summer$recruit_est and shlfvol_summer$mean_v_lag1
## t = 0.42475, df = 53, p-value = 0.6727
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2103017 0.3186189
## sample estimates:
## cor
## 0.05824508ggscatter(shlfvol_summer, x = "recruit_est", y = "mean_v_lag1",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean shelf water volume",
title="One year lag")
## shelf water volume - winter
lm_winter<-lm(mean_v ~ recruit_est, data=shlfvol_winter)
summary(lm_winter)##
## Call:
## lm(formula = mean_v ~ recruit_est, data = shlfvol_winter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1825.94 -408.58 -48.77 589.87 1899.07
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3561.62229317 252.97063784 14.079 <0.0000000000000002 ***
## recruit_est 0.00008842 0.00015636 0.565 0.574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 849.7 on 53 degrees of freedom
## Multiple R-squared: 0.005997, Adjusted R-squared: -0.01276
## F-statistic: 0.3198 on 1 and 53 DF, p-value: 0.5741cor.test(shlfvol_winter$recruit_est, shlfvol_winter$mean_v, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: shlfvol_winter$recruit_est and shlfvol_winter$mean_v
## t = 0.56548, df = 53, p-value = 0.5741
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1917969 0.3358382
## sample estimates:
## cor
## 0.07744072ggscatter(shlfvol_winter, x = "recruit_est", y = "mean_v",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean shelf water volume",
title="Winter")
## shelf water volume - spring
lm_spring<-lm(mean_v ~ recruit_est, data=shlfvol_spring)
summary(lm_spring)##
## Call:
## lm(formula = mean_v ~ recruit_est, data = shlfvol_spring)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1551.4 -217.0 106.6 371.5 1058.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4302.6869130 247.4162454 17.39 <0.0000000000000002 ***
## recruit_est -0.0001105 0.0001492 -0.74 0.465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 655.8 on 29 degrees of freedom
## Multiple R-squared: 0.01856, Adjusted R-squared: -0.01529
## F-statistic: 0.5483 on 1 and 29 DF, p-value: 0.465cor.test(shlfvol_spring$recruit_est, shlfvol_spring$mean_v, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: shlfvol_spring$recruit_est and shlfvol_spring$mean_v
## t = -0.74048, df = 29, p-value = 0.465
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4679736 0.2291804
## sample estimates:
## cor
## -0.136222ggscatter(shlfvol_spring, x = "recruit_est", y = "mean_v",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean shelf water volume",
title="spring")
## shelf water volume - summer
lm_summer<-lm(mean_v ~ recruit_est, data=shlfvol_summer)
summary(lm_summer)##
## Call:
## lm(formula = mean_v ~ recruit_est, data = shlfvol_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1826.3 -517.7 -138.3 392.3 1634.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4286.7960555 238.2156279 17.995 <0.0000000000000002 ***
## recruit_est -0.0001007 0.0001489 -0.676 0.501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 823.4 on 58 degrees of freedom
## Multiple R-squared: 0.007827, Adjusted R-squared: -0.00928
## F-statistic: 0.4575 on 1 and 58 DF, p-value: 0.5015cor.test(shlfvol_summer$recruit_est, shlfvol_summer$mean_v, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: shlfvol_summer$recruit_est and shlfvol_summer$mean_v
## t = -0.67641, df = 58, p-value = 0.5015
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3348709 0.1692581
## sample estimates:
## cor
## -0.08846888ggscatter(shlfvol_summer, x = "recruit_est", y = "mean_v",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean shelf water volume",
title="Summer")
## shelf water volume - fall
lm_fall<-lm(mean_v ~ recruit_est, data=shlfvol_fall)
summary(lm_fall)##
## Call:
## lm(formula = mean_v ~ recruit_est, data = shlfvol_fall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1503.4 -637.8 -157.9 701.3 1399.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3417.26789663 356.73487609 9.579 0.000000000515 ***
## recruit_est 0.00004819 0.00020238 0.238 0.814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 874.2 on 26 degrees of freedom
## Multiple R-squared: 0.002176, Adjusted R-squared: -0.0362
## F-statistic: 0.0567 on 1 and 26 DF, p-value: 0.8137cor.test(shlfvol_fall$recruit_est, shlfvol_fall$mean_v, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: shlfvol_fall$recruit_est and shlfvol_fall$mean_v
## t = 0.23811, df = 26, p-value = 0.8137
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3322114 0.4125444
## sample estimates:
## cor
## 0.046647ggscatter(shlfvol_fall, x = "recruit_est", y = "mean_v",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean shelf water volume",
title="Fall")
## shelf water temperature
lm_shlftemp<-lm(mean_t ~ recruit_est, data=df.join.shlf)
summary(lm_shlftemp)##
## Call:
## lm(formula = mean_t ~ recruit_est, data = df.join.shlf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8944 -4.4381 0.5717 3.8118 7.3266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.93329366726 0.70173440985 15.580 <0.0000000000000002 ***
## recruit_est 0.00000004946 0.00000042705 0.116 0.908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.237 on 172 degrees of freedom
## Multiple R-squared: 7.797e-05, Adjusted R-squared: -0.005736
## F-statistic: 0.01341 on 1 and 172 DF, p-value: 0.9079cor.test(df.join.shlf$recruit_est, df.join.shlf$mean_t, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: df.join.shlf$recruit_est and df.join.shlf$mean_t
## t = 0.11581, df = 172, p-value = 0.9079
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1401238 0.1573931
## sample estimates:
## cor
## 0.008830066ggscatter(df.join.shlf, x = "recruit_est", y = "mean_t",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean shelf water temperature")
## shelf water salinity
lm_shlfsal<-lm(mean_s ~ recruit_est, data=df.join.shlf)
summary(lm_shlfsal)##
## Call:
## lm(formula = mean_s ~ recruit_est, data = df.join.shlf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9007 -0.1919 0.0226 0.2090 0.6810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.84056244573 0.05441122191 603.562 <0.0000000000000002 ***
## recruit_est -0.00000001769 0.00000003311 -0.534 0.594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3286 on 172 degrees of freedom
## Multiple R-squared: 0.001657, Adjusted R-squared: -0.004147
## F-statistic: 0.2855 on 1 and 172 DF, p-value: 0.5938cor.test(df.join.shlf$recruit_est, df.join.shlf$mean_s, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: df.join.shlf$recruit_est and df.join.shlf$mean_s
## t = -0.53436, df = 172, p-value = 0.5938
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1883400 0.1087174
## sample estimates:
## cor
## -0.04071088ggscatter(df.join.shlf, x = "recruit_est", y = "mean_s",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean shelf water salinity")
Gulf-stream index
Gulf stream index was calculated based on method presented by Pérez-Hernández and Joyce (2014). The gulf stream index (GSI) is a measure of the degrees latitude above the average Gulf Stream position based on ocean temperature at 200m (15 C) depth between 55W to 75W.
Positive values indicate a the mean position of the GS is more Northernly, whereas negative values indicate a more Southernly position.
# positive are more Northerly, negative are more southernly
## --- Note below is the original data source, which was modified to include
## --- month and then saved as a new file (mm_gsi_1954_2022_chen.csv).
# gsi.a <- ecodata::gsi
# gsi.m <- read.csv(here::here('data/gulf_stream_index/Chen_EN4_T200_GSI_1954_2022_monthly - Zhuomin Chen.xlsx - Sheet1.csv'))
# # weird inefficient solution I came up with to get month values (but it works)
# gsi.m$year <- as.numeric(gsub("(^\\d{4}).*", "\\1", gsi.m$Month))
# gsi.m$m.1 <- round(str_extract(gsi.m$Month, '\\d+([.,]\\d+)?') %>% as.numeric()- gsi.m$year,2)
# gsi.m$month <- round(as.numeric(str_extract(gsi.m$m.1, '\\d\\d')))
# is.na(gsi.m$month) <- 10
# gsi.m$month <- replace(gsi.m$month, is.na(gsi.m$month), 10)
# gsi.m <- gsi.m[,-4]
# # saving so don't have to do that everytime:
# write.csv(gsi.m, 'mm_gsi_1954_2022_chen.csv') #
gsi.m <- read.csv(here::here('data/gulf_stream_index/mm_gsi_1954_2022_chen.csv'))
# df <- dplyr::full_join(recruit, gsi.m, by = join_by(year)) %>%
# dplyr::select(year, month, recruit_est, GSI) %>%
# filter(month %in% c(3:9)) %>%
# tidyr::pivot_wider(names_from = c(month),
# values_from = c(GSI)) %>%
# rlang::set_names(c('year','recruit_est','Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep')) %>%
# tidyr::drop_na()
## --- This joins recruit estimate data to GSI data and
## --- calculates some summary stats(mean, sd, min, max)
df <- dplyr::full_join(recruit, gsi.m %>%
group_by(year) %>%
filter(month %in% c(3:8)) %>%
summarise(m.gsi = mean(GSI),
sd.gsi = sd(GSI),
max.gsi = max(GSI),
min.gsi = min(GSI)),
by = join_by(year)) %>%
mutate(pos = ifelse(m.gsi > 0, 'Northerly', 'Southerly'),
n.pos = ifelse(m.gsi > 0, 1, 0))
df <- df[-c(51:69),] # this removes rows w/years that don't match
# (could/should do this before hand in more standardized way - will do later)
# Plot the time series (mean GSI for the months of interest)
# -- Note: here I am looking just at March through August assuming these
# months are the most important to recently spawned individuals
ggplot(data = df,
aes(x = year, y = m.gsi)) +
geom_line(lwd = 1) +
geom_hline(yintercept = 0, lty = 2) +
labs(title = 'Mean GS position anomaly March:August',
x = 'Year',
y = "Gulf stream position anomaly\n") +
theme_bw() 
# dplyr::full_join(recruit, gsi.m, by = join_by(year)) %>%
# dplyr::select(year, month, recruit_est, GSI) %>%
# #filter(month %in% c(3:9)) %>%
# filter(year>1997) %>%
# tidyr::drop_na() %>%
# ggplot2::ggplot(.,aes(x=recruit_est, y=GSI)) +
# geom_point(color = 'black') +
# xlab('Recruitment estimate') +
# ylab('Gulf stream position anomaly') +
# labs(title = 'Gulf stream index') +
# geom_hline(yintercept = 0, lty = 2) +
# theme_bw()
# Looking across all months
dplyr::full_join(recruit, gsi.m, by = join_by(year)) %>%
dplyr::select(year, month, recruit_est, GSI) %>%
#filter(month %in% c(3:9)) %>%
filter(year>1997) %>%
tidyr::drop_na() %>%
ggplot2::ggplot(.,aes(x=recruit_est, y=GSI)) +
geom_point(color = 'black') +
facet_wrap(~month)+
xlab('Recruitment estimate') +
ylab('Gulf stream position anomaly') +
labs(title = 'Gulf stream index by month') +
geom_hline(yintercept = 0, lty = 2) +
theme_bw()
# Looking across April through July
dplyr::full_join(recruit, gsi.m, by = join_by(year)) %>%
dplyr::select(year, month, recruit_est, GSI) %>%
#filter(month %in% c(3:9)) %>%
filter(year>1997 & month %in% c(4:7)) %>%
tidyr::drop_na() %>%
ggplot2::ggplot(.,aes(x=recruit_est, y=GSI)) +
geom_point(color = 'black') +
xlab('Recruitment estimate') +
ylab('Gulf stream position anomaly') +
labs(title = 'Gulf stream index (Apr:Jul)') +
geom_hline(yintercept = 0, lty = 2) +
theme_bw()
Correlations
df.gsi = dplyr::full_join(recruit, gsi.m, by = join_by(year)) %>%
dplyr::select(year, month, recruit_est, GSI) %>%
mutate(gsi_lag1 = lag(GSI,12)) %>%
filter(year>1997) %>%
tidyr::drop_na() #GSI by month and with year lag
#by season
gsi_winter <- filter(df.gsi, month %in% c(1,2,3))
gsi_spring <- filter(df.gsi, month %in% c(4,5,6))
gsi_summer <- filter(df.gsi, month %in% c(7,8,9))
gsi_fall <- filter(df.gsi, month %in% c(10,11,12))
## GSI - one year lag
lm_lag<-lm(gsi_lag1 ~ recruit_est, data=gsi_summer)
summary(lm_lag)##
## Call:
## lm(formula = gsi_lag1 ~ recruit_est, data = gsi_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.25835 -0.53882 -0.09034 0.50124 1.57858
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.57001628051 0.19176413511 2.972 0.0041 **
## recruit_est -0.00000004877 0.00000011817 -0.413 0.6812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7602 on 67 degrees of freedom
## Multiple R-squared: 0.002536, Adjusted R-squared: -0.01235
## F-statistic: 0.1703 on 1 and 67 DF, p-value: 0.6812cor.test(gsi_summer$recruit_est, gsi_summer$gsi_lag1, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: gsi_summer$recruit_est and gsi_summer$gsi_lag1
## t = -0.41269, df = 67, p-value = 0.6812
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2836542 0.1885740
## sample estimates:
## cor
## -0.05035404ggscatter(gsi_summer, x = "recruit_est", y = "gsi_lag1",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Gulf Stream Position Anomaly",
title="One year lag")
## GSI - winter
lm_winter<-lm(GSI ~ recruit_est, data=gsi_winter)
summary(lm_winter)##
## Call:
## lm(formula = GSI ~ recruit_est, data = gsi_winter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.16418 -0.68857 0.00435 0.78957 1.61442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3965499997 0.2241478634 1.769 0.0814 .
## recruit_est 0.0000001343 0.0000001381 0.972 0.3345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8886 on 67 degrees of freedom
## Multiple R-squared: 0.01391, Adjusted R-squared: -0.0008119
## F-statistic: 0.9448 on 1 and 67 DF, p-value: 0.3345cor.test(gsi_winter$recruit_est, gsi_winter$GSI, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: gsi_winter$recruit_est and gsi_winter$GSI
## t = 0.97203, df = 67, p-value = 0.3345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1221670 0.3449758
## sample estimates:
## cor
## 0.1179234ggscatter(gsi_winter, x = "recruit_est", y = "GSI",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Gulf Stream Position Anomaly",
title="Winter")
## GSI - spring
lm_spring<-lm(GSI ~ recruit_est, data=gsi_spring)
summary(lm_spring)##
## Call:
## lm(formula = GSI ~ recruit_est, data = gsi_spring)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8868 -0.3629 -0.0042 0.4637 1.2688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3252916609 0.1844570954 1.764 0.0824 .
## recruit_est 0.0000001557 0.0000001137 1.370 0.1754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7313 on 67 degrees of freedom
## Multiple R-squared: 0.02723, Adjusted R-squared: 0.01271
## F-statistic: 1.876 on 1 and 67 DF, p-value: 0.1754cor.test(gsi_spring$recruit_est, gsi_spring$GSI, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: gsi_spring$recruit_est and gsi_spring$GSI
## t = 1.3695, df = 67, p-value = 0.1754
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07457117 0.38660302
## sample estimates:
## cor
## 0.1650221ggscatter(gsi_spring, x = "recruit_est", y = "GSI",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Gulf Stream Position Anomaly",
title="spring")
## GSI - summer
lm_summer<-lm(GSI ~ recruit_est, data=gsi_summer)
summary(lm_summer)##
## Call:
## lm(formula = GSI ~ recruit_est, data = gsi_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3212 -0.5852 -0.1173 0.4797 1.5880
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.49183735979 0.19059133130 2.581 0.0121 *
## recruit_est 0.00000004398 0.00000011744 0.374 0.7092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7556 on 67 degrees of freedom
## Multiple R-squared: 0.002089, Adjusted R-squared: -0.01281
## F-statistic: 0.1402 on 1 and 67 DF, p-value: 0.7092cor.test(gsi_summer$recruit_est, gsi_summer$GSI, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: gsi_summer$recruit_est and gsi_summer$GSI
## t = 0.37448, df = 67, p-value = 0.7092
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1930668 0.2793612
## sample estimates:
## cor
## 0.04570227ggscatter(gsi_summer, x = "recruit_est", y = "GSI",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Gulf Stream Position Anomaly",
title="summer")
## GSI - fall
lm_fall<-lm(GSI ~ recruit_est, data=gsi_fall)
summary(lm_fall)##
## Call:
## lm(formula = GSI ~ recruit_est, data = gsi_fall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0182 -0.6567 0.1503 0.5979 1.5617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55723961229 0.20548181784 2.712 0.00849 **
## recruit_est 0.00000004091 0.00000012662 0.323 0.74762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8146 on 67 degrees of freedom
## Multiple R-squared: 0.001556, Adjusted R-squared: -0.01335
## F-statistic: 0.1044 on 1 and 67 DF, p-value: 0.7476cor.test(gsi_fall$recruit_est, gsi_fall$GSI, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: gsi_fall$recruit_est and gsi_fall$GSI
## t = 0.3231, df = 67, p-value = 0.7476
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1990968 0.2735694
## sample estimates:
## cor
## 0.03944253ggscatter(gsi_fall, x = "recruit_est", y = "GSI",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Gulf Stream Position Anomaly",
title="Fall")
Should I add a lag? If so, how much? From Nesslage_ea_21: For the Northern stock- From the original 49 explanatory variables, the final RF model included 10 variables: annual AMO (lagged 5–7 years); December to April AMO (lagged 5–7 years); station-based December to February NAO (lagged 3 and 4 years); PC-based December to February NAO (lagged 4 years), and management time block (Table 1; Figure S3). The final GAMM based on backward selection of variables in- cluded December to April AMO lagged 7 years and station-based December to February NAO lagged 3 and 4 years (Table 1). The shapes of the relationships approximated by the RF and GAMM indi- cate that golden tilefish landings were higher during negative AMO and positive NAO, with their respective lags (Figures 2 and 3). The largest range of the smoothed term on the y-axis corresponded with December to April AMO lagged 7 years (Figures 2 and 3) and this co- variate contributed 52.5% of the GAM R2 (Figure 4), implying AMO has the largest influence on northern landings. In contrast, NAO co-variates at lags of 3 and 4 years contributed a combined 47.5% of the GAM R2 (Figure 4).
The random forest using the full dataset identified 62 significant variables from the original 121 explanatory variables (Table 1), including two versions of the AMO (annual and seasonal with each lagged 0–7 years), both seasonal versions of PC-based NAO (each lagged 0–7 years), Labrador Current transport indices (NE Track 191: lagged 0, 4, 9, and 10 quarters), Gulf Stream index of position anomalies (lagged 0, 1, 4–10, and 12 quarters), Gulf stream position indices (lagged 0–3 years), Gulf stream transport index (lagged 0–3 years), bottom temperature anomalies (lagged 0–2, 4–7 years), and time block (Figure S1). The final GAMM for northern CPUE in- cluded four variables: annual AMO lagged 6 years, December to April AMO lagged 7 years, Gulf Stream index of position anomalies lagged 12 quarters, and the Labrador Current transport index for NE Track 191 unlagged (Table 1; Figure 5). Annual AMO lagged 6 years and December to April AMO lagged 7 years contributed a combined 64.1% of the GAM R2(Figure 4). Gulf Stream and Labrador Current transport indices contributed only 19.7% and 16.2%, respectively, of the GAM R2(Figure 4).
# recruitment index across all years
dplyr::full_join(recruit, gsi.m %>%
group_by(year) %>%
filter(month %in% c(3:8)) %>%
summarise(m.gsi = mean(GSI)),
by = join_by(year)) %>%
ggplot2::ggplot(., aes(x=recruit_est, y=m.gsi)) +
geom_point(color = 'black') +
geom_hline(yintercept = 0, lty = 2)+
labs(title = 'All years') +
xlab('Recruitment estimate') +
ylab('Gulf stream position anomaly') +
geom_smooth(method = "lm", formula = y ~ x, size = 1, se = FALSE,
aes(colour = 'Linear')) +
geom_smooth(method = "lm", formula = y ~ x + I(x^2),
size = 1, se = FALSE, aes(colour = 'Quadratic')) +
geom_smooth(method = "loess", formula = y ~ x,
size = 1, se = FALSE, aes(colour = 'Loess')) +
geom_smooth(method = "gam", formula = y ~ s(x),
size = 1, se = FALSE, aes(colour = 'Gam')) +
geom_smooth(method = "gam", formula = y ~ s(x, k = 3),
size = 1, se = FALSE, aes(colour = 'Gam2')) +
scale_color_manual(name='Model',
breaks=c('Linear', 'Quadratic', 'Loess', 'Gam', 'Gam2'),
values=c('Linear'='black', 'Quadratic'='blue',
'Loess'='red', 'Gam' = 'green',
'Gam2' = 'purple')) +
theme_bw()
# recruitment index across just the years that Paul recommended + el nino 1998
tt = dplyr::full_join(recruit, gsi.m %>%
group_by(year) %>%
filter(month %in% c(3:8)) %>%
summarise(m.gsi = mean(GSI)),
by = join_by(year))
ggplot2::ggplot(tt %>% filter(year>1997), aes(x=recruit_est, y=m.gsi)) +
geom_point(color = 'black') +
geom_hline(yintercept = 0, lty = 2)+
labs(title = '1998:2020, No outliers') +
xlab('Recruitment estimate') +
ylab('Gulf stream position anomaly') +
geom_smooth(method = "lm", formula = y ~ x, size = 1, se = FALSE,
aes(colour = 'Linear')) +
geom_smooth(method = "lm", formula = y ~ x + I(x^2),
size = 1, se = FALSE, aes(colour = 'Quadratic')) +
geom_smooth(method = "loess", formula = y ~ x,
size = 1, se = FALSE, aes(colour = 'Loess')) +
geom_smooth(method = "gam", formula = y ~ s(x),
size = 1, se = FALSE, aes(colour = 'Gam')) +
geom_smooth(method = "gam", formula = y ~ s(x, k = 3),
size = 1, se = FALSE, aes(colour = 'Gam2')) +
scale_color_manual(name='Model',
breaks=c('Linear', 'Quadratic', 'Loess', 'Gam', 'Gam2'),
values=c('Linear'='black', 'Quadratic'='blue',
'Loess'='red', 'Gam' = 'green',
'Gam2' = 'purple')) +
theme_bw()
ggplot(data = df, # add the data
aes(x = year, y = m.gsi, # set x, y coordinates
color = pos)) + # color by GS position
geom_boxplot() +
facet_grid(~pos) + # create panes base on GS position
ecodata::theme_facet()
# this gives main effects AND interactions
pos_aov <- aov(recruit_est ~ year * pos,
data = df)
# look at effects and interactions
# summary(pos_aov)
tidy_pos_aov <- broom::tidy(pos_aov)
tidy_pos_aov## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 year 1 3.85e11 384693730334. 0.717 0.401
## 2 pos 1 5.37e10 53734167452. 0.100 0.753
## 3 year:pos 1 6.82e11 682304704182. 1.27 0.265
## 4 Residuals 46 2.47e13 536176435236. NA NA# write.csv(tidy_pos_aov, 'gs_pos_aov.csv')
ggplot(data = df,
aes(x = recruit_est/1000, y = m.gsi, fill = pos,
group = year)) +
geom_bar(color = "black", stat = "identity",
position = position_dodge2(preserve = "single"), width = 20) +
theme_bw() +
labs(title = 'All years',
x = "\nRecruitment estimate (x1000)",
y = "Gulf stream position anomaly\n")
ggplot(data = df %>% filter(year <= 1999),
aes(x = recruit_est/1000, y = m.gsi, fill = pos,
group = year)) +
geom_bar(color = "black", stat = "identity",
position = position_dodge2(preserve = "single"), width = 40) +
theme_bw() +
labs(title = '1971:1999',
x = "\nRecruitment estimate (x1000)",
y = "Gulf stream position anomaly\n")
ggplot(data = df %>% filter(year >1999),
aes(x = recruit_est/1000, y = m.gsi, fill = pos,
group = year)) +
geom_bar(color = "black", stat = "identity",
position = position_dodge2(preserve = "single"), width = 40) +
theme_bw() +
labs(title = '2000:2020',
x = "\nRecruitment estimate (x1000)",
y = "Gulf stream position anomaly\n")
Food availablity
Larval tilefish eat zooplankton, likely calanus. Calanus finmarchicus are a copepod (crustacean) with a one-year life cycle and are an important food source for many commercially important species. Calanus spp. are lipid rich, herbivorous species that eat phytoplankton, diatoms in particular (Hobbs et al. 2020).
Diatoms are often represented as microplankton (>20 µm), but many species are of the nanoplankton size class (2-20 µm), and a smaller few may even overlap with picoplanton size class (<2 µm).
Calanus
Calanus is not as common in MAB, need to figure out dominant zooplankton in MAB.
# Calanus
calanus <- ecodata::calanus_stage %>% filter(Time %in% c(1998:2021))%>%
rename_all(., .funs = tolower) %>%
mutate(year = time)
ggplot() +
geom_line(data = calanus %>% filter(epu == 'GB',
var == 'adt-Spring'),
aes(x = year , y = value, col = epu), lwd = 1) +
geom_line(data = calanus %>% filter(epu == 'MAB',
var == 'adt-Spring'),
aes(x = year , y = value, col = epu), lwd = 1) +
labs(color = c('EPU')) +
theme_minimal()
Georges Bank
# GB
c5.gb <- calanus %>% filter(epu == 'GB', var == 'c5-Spring')
adult.gb <- calanus %>% filter(epu == 'GB', var == 'adt-Spring' )
df.c5 <- dplyr::full_join(recruit, c5.gb, by = join_by(year)) %>%
dplyr::select(year, recruit_est, value) %>%
tidyr::drop_na()
df.adt <- dplyr::full_join(recruit, adult.gb, by = join_by(year)) %>%
dplyr::select(year, recruit_est, value) %>%
tidyr::drop_na()
# Regression
ggscatter(df.c5, x = 'recruit_est', y = 'value',
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Recruitment estimate",
ylab = "Calanus c5 spring (No. per 100m^-3)",
title = 'c5') 
ggscatter(df.adt, x = 'recruit_est', y = 'value',
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Recruitment estimate",
ylab = "Calanus adult spring (No. per 100m^-3)",
title = 'Adult') 
# GLM
eqn <- as.formula(paste('recruit_est ~', paste(colnames(df.c5)[1],
collapse = " + ")))
mod0 <- glm(recruit_est ~ 1,
data = df.c5,
family = "poisson")
mod1 <- glm(eqn,
data = df.c5,
family = "poisson")
summary(mod0)##
## Call:
## glm(formula = recruit_est ~ 1, family = "poisson", data = df.c5)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 14.1979354 0.0001802 78775 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 8933474 on 20 degrees of freedom
## Residual deviance: 8933474 on 20 degrees of freedom
## AIC: 8933809
##
## Number of Fisher Scoring iterations: 4summary(mod1)##
## Call:
## glm(formula = eqn, family = "poisson", data = df.c5)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 43.50429247 0.05804867 749.4 <0.0000000000000002 ***
## year -0.01459584 0.00002891 -504.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 8933474 on 20 degrees of freedom
## Residual deviance: 8677368 on 19 degrees of freedom
## AIC: 8677705
##
## Number of Fisher Scoring iterations: 4AIC(mod0, mod1) %>% dplyr::arrange(AIC)## df AIC
## mod1 2 8677705
## mod0 1 8933809null_prediction <- exp(predict(mod0))
mod_prediction <- exp(predict(mod1))
plot(df.c5$year, df.c5$recruit_est, type = 'l')
lines(df.c5$year, null_prediction, col = "gray")
lines(df.c5$year, mod_prediction, col = "red")
Mid-atlantic
# Mid-Atlantic Bight
c5.mab <- calanus %>% filter(epu == 'MAB', var == 'c5-Spring')
adult.mab <- calanus %>% filter(epu == 'MAB', var == 'adt-Spring' )
df.c5 <- dplyr::full_join(recruit, c5.mab, by = join_by(year)) %>%
dplyr::select(year, recruit_est, value) %>%
tidyr::drop_na()
df.adt <- dplyr::full_join(recruit, adult.mab, by = join_by(year)) %>%
dplyr::select(year, recruit_est, value) %>%
tidyr::drop_na()
# Regression
ggscatter(df.c5, x = 'recruit_est', y = 'value',
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Recruitment estimate",
ylab = "Calanus c5 spring (No. per 100m^-3)",
title = 'c5') 
ggscatter(df.adt, x = 'recruit_est', y = 'value',
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Recruitment estimate",
ylab = "Calanus adult spring (No. per 100m^-3)",
title = 'Adult') 
Microplankton
micro<-read.csv(here::here('data/phyto_size_class/microplankton_ts_gtf_strata.csv'))Microplankton maps
# microplanktonMicroplankton analysis
# Microplankton
# micro with year lag
micro.lag <- micro %>%
mutate(micro_lag1 = lag(weighted_mean_micro,12))
# Join with recruit estimate
micro.rec <- dplyr::full_join(recruit, micro.lag %>%
group_by(year) %>%
filter(year %in% c(1998:2020)),
dplyr::select(year, month, mean_cpue, mean_micro, weighted_mean_micro, micro_lag1),
by = join_by(year))
micro.rec <- micro.rec[-c(1:27),] #removes year < 1998 (starting year for analysis)
#by season
micro_winter <- filter(micro.rec, month %in% c(1,2,3))
micro_winter <- micro_winter[-c(18),] # removes outlier
micro_spring <- filter(micro.rec, month %in% c(4,5,6))
micro_spring <- micro_spring[-c(7),]
micro_summer <- filter(micro.rec, month %in% c(7,8,9))
micro_summer <- micro_summer[-c(17),] #removes outlier
micro_fall <- filter(micro.rec, month %in% c(10,11,12))
micro_fall <- micro_fall[-c(62),] #removes outlier
## One year lag - Summer (peak spawn)
lm_lag<-lm(micro_lag1 ~ recruit_est, data=micro_summer)
summary(lm_lag)##
## Call:
## lm(formula = micro_lag1 ~ recruit_est, data = micro_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059018 -0.024279 -0.004239 0.013179 0.136674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.110189251533 0.009156148372 12.03 <0.0000000000000002 ***
## recruit_est -0.000000004544 0.000000005613 -0.81 0.421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03558 on 63 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.0103, Adjusted R-squared: -0.005414
## F-statistic: 0.6554 on 1 and 63 DF, p-value: 0.4212cor.test(micro_summer$recruit_est, micro_summer$micro_lag1, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: micro_summer$recruit_est and micro_summer$micro_lag1
## t = -0.80954, df = 63, p-value = 0.4212
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3370252 0.1460467
## sample estimates:
## cor
## -0.1014667ggscatter(micro_summer, x = "recruit_est", y = "micro_lag1",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean Microplankton",
title="One Year Lag")
## Winter
lm_winter<-lm(weighted_mean_micro ~ recruit_est, data=micro_winter)
summary(lm_winter)##
## Call:
## lm(formula = weighted_mean_micro ~ recruit_est, data = micro_winter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15902 -0.05443 -0.02121 0.04641 0.24403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31258335094 0.02038050717 15.337 <0.0000000000000002 ***
## recruit_est 0.00000001945 0.00000001247 1.559 0.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07927 on 66 degrees of freedom
## Multiple R-squared: 0.03552, Adjusted R-squared: 0.02091
## F-statistic: 2.431 on 1 and 66 DF, p-value: 0.1238cor.test(micro_winter$recruit_est, micro_winter$weighted_mean_micro, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: micro_winter$recruit_est and micro_winter$weighted_mean_micro
## t = 1.5591, df = 66, p-value = 0.1238
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05230153 0.40854039
## sample estimates:
## cor
## 0.1884738ggscatter(micro_winter, x = "recruit_est", y = "weighted_mean_micro",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Winter Weighted Mean Microplankton",
title="Winter")
## Spring
lm_spring<-lm(weighted_mean_micro ~ recruit_est, data=micro_spring)
summary(lm_spring)##
## Call:
## lm(formula = weighted_mean_micro ~ recruit_est, data = micro_spring)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23260 -0.14253 -0.02222 0.12166 0.36330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37086194703 0.03992439569 9.289 0.000000000000134 ***
## recruit_est -0.00000002326 0.00000002501 -0.930 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1565 on 66 degrees of freedom
## Multiple R-squared: 0.01293, Adjusted R-squared: -0.002025
## F-statistic: 0.8646 on 1 and 66 DF, p-value: 0.3559cor.test(micro_spring$recruit_est, micro_spring$weighted_mean_micro, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: micro_spring$recruit_est and micro_spring$weighted_mean_micro
## t = -0.92982, df = 66, p-value = 0.3559
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3428412 0.1281894
## sample estimates:
## cor
## -0.1137111ggscatter(micro_spring, x = "recruit_est", y = "weighted_mean_micro",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Spring Weighted Mean Microplankton",
title="Spring")
## Summer
lm_summer<-lm(weighted_mean_micro ~ recruit_est, data=micro_summer)
summary(lm_summer)##
## Call:
## lm(formula = weighted_mean_micro ~ recruit_est, data = micro_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049815 -0.020294 -0.005428 0.014549 0.091153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.104255417569 0.007883909284 13.224 <0.0000000000000002 ***
## recruit_est -0.000000002676 0.000000004825 -0.555 0.581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03066 on 66 degrees of freedom
## Multiple R-squared: 0.004638, Adjusted R-squared: -0.01044
## F-statistic: 0.3075 on 1 and 66 DF, p-value: 0.5811cor.test(micro_summer$recruit_est, micro_summer$weighted_mean_micro, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: micro_summer$recruit_est and micro_summer$weighted_mean_micro
## t = -0.55455, df = 66, p-value = 0.5811
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3016296 0.1731342
## sample estimates:
## cor
## -0.06810209ggscatter(micro_summer, x = "recruit_est", y = "weighted_mean_micro",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean Microplankton",
title="Summer")
## Fall
lm_fall<-lm(weighted_mean_micro ~ recruit_est, data=micro_fall)
summary(lm_fall)##
## Call:
## lm(formula = weighted_mean_micro ~ recruit_est, data = micro_fall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22693 -0.10823 -0.00688 0.06245 0.48119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27273185989 0.03558291697 7.665 0.000000000105 ***
## recruit_est 0.00000002725 0.00000002203 1.237 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.141 on 66 degrees of freedom
## Multiple R-squared: 0.02267, Adjusted R-squared: 0.007858
## F-statistic: 1.531 on 1 and 66 DF, p-value: 0.2204cor.test(micro_fall$recruit_est, micro_fall$weighted_mean_micro, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: micro_fall$recruit_est and micro_fall$weighted_mean_micro
## t = 1.2372, df = 66, p-value = 0.2204
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09114366 0.37549939
## sample estimates:
## cor
## 0.1505531ggscatter(micro_fall, x = "recruit_est", y = "weighted_mean_micro",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Fall Weighted Mean Microplankton",
title="Fall")
Chlorophyll-A
# CHL-A
chl<-read.csv(here::here('data/chl/chl_ts_gtf_strata.csv'))
# chl with year lag
chl.lag <- chl %>%
mutate(chl_lag1 = lag(weighted_mean_chl,12))
# Join with recruit estimate
chl.rec <- dplyr::full_join(recruit, chl.lag %>%
group_by(year) %>%
filter(year %in% c(1998:2020)),
dplyr::select(year, month, mean_cpue, mean_chl, weighted_mean_chl, chl_lag1),
by = join_by(year))
chl.rec <- chl.rec[-c(1:27),] #removes year < 1998 (starting year for analysis)Maps (Chlorophyll-A)
# CHL-AChlorophyll-A analysis
# CHL-A
#by season
chl_winter <- filter(chl.rec, month %in% c(1,2,3))
chl_winter <- chl_winter[-c(18),] # removes outlier
chl_spring <- filter(chl.rec, month %in% c(4,5,6))
#chl_spring <- chl_spring[-c(7),]
chl_summer <- filter(chl.rec, month %in% c(7,8,9))
#chl_summer <- chl_summer[-c(17),] #removes outlier
chl_fall <- filter(chl.rec, month %in% c(10,11,12))
chl_fall <- chl_fall[-c(62),] #removes outlier
## One year lag - Summer (peak spawn)
lm_lag<-lm(chl_lag1 ~ recruit_est, data=chl_summer)
summary(lm_lag)##
## Call:
## lm(formula = chl_lag1 ~ recruit_est, data = chl_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.173365 -0.064682 -0.004704 0.051986 0.271157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37686015744 0.02296955235 16.407 <0.0000000000000002 ***
## recruit_est -0.00000001002 0.00000001418 -0.707 0.482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09099 on 64 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.007747, Adjusted R-squared: -0.007756
## F-statistic: 0.4997 on 1 and 64 DF, p-value: 0.4822cor.test(chl_summer$recruit_est, chl_summer$chl_lag1, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: chl_summer$recruit_est and chl_summer$chl_lag1
## t = -0.7069, df = 64, p-value = 0.4822
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3231677 0.1573656
## sample estimates:
## cor
## -0.0880196ggscatter(chl_summer, x = "recruit_est", y = "chl_lag1",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean Chlorophyll",
title="One Year Lag")
## Winter
lm_winter<-lm(weighted_mean_chl ~ recruit_est, data=chl_winter)
summary(lm_winter)##
## Call:
## lm(formula = weighted_mean_chl ~ recruit_est, data = chl_winter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141707 -0.073832 -0.008945 0.063666 0.284785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.64421482683 0.02443803558 26.361 <0.0000000000000002 ***
## recruit_est 0.00000001345 0.00000001496 0.899 0.372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09505 on 66 degrees of freedom
## Multiple R-squared: 0.0121, Adjusted R-squared: -0.002866
## F-statistic: 0.8085 on 1 and 66 DF, p-value: 0.3718cor.test(chl_winter$recruit_est, chl_winter$weighted_mean_chl, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: chl_winter$recruit_est and chl_winter$weighted_mean_chl
## t = 0.89917, df = 66, p-value = 0.3718
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1318753 0.3395284
## sample estimates:
## cor
## 0.1100087ggscatter(chl_winter, x = "recruit_est", y = "weighted_mean_chl",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Winter Weighted Mean Chlorophyll",
title="Winter")
## Spring
lm_spring<-lm(weighted_mean_chl ~ recruit_est, data=chl_spring)
summary(lm_spring)##
## Call:
## lm(formula = weighted_mean_chl ~ recruit_est, data = chl_spring)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36925 -0.14812 -0.00048 0.13185 0.39266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.72477990092 0.04659591460 15.555 <0.0000000000000002 ***
## recruit_est -0.00000001770 0.00000002871 -0.616 0.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1847 on 67 degrees of freedom
## Multiple R-squared: 0.005639, Adjusted R-squared: -0.009203
## F-statistic: 0.3799 on 1 and 67 DF, p-value: 0.5397cor.test(chl_spring$recruit_est, chl_spring$weighted_mean_chl, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: chl_spring$recruit_est and chl_spring$weighted_mean_chl
## t = -0.61639, df = 67, p-value = 0.5397
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3063278 0.1645132
## sample estimates:
## cor
## -0.07509132ggscatter(chl_spring, x = "recruit_est", y = "weighted_mean_chl",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Spring Weighted Mean Chlorophyll",
title="Spring")
## Summer
lm_summer<-lm(weighted_mean_chl ~ recruit_est, data=chl_summer)
summary(lm_summer)##
## Call:
## lm(formula = weighted_mean_chl ~ recruit_est, data = chl_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14300 -0.06812 -0.01667 0.05040 0.26048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.38760080035 0.02240932962 17.296 <0.0000000000000002 ***
## recruit_est -0.00000001922 0.00000001381 -1.392 0.169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08884 on 67 degrees of freedom
## Multiple R-squared: 0.02809, Adjusted R-squared: 0.01358
## F-statistic: 1.936 on 1 and 67 DF, p-value: 0.1687cor.test(chl_summer$recruit_est, chl_summer$weighted_mean_chl, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: chl_summer$recruit_est and chl_summer$weighted_mean_chl
## t = -1.3915, df = 67, p-value = 0.1687
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.38885569 0.07193415
## sample estimates:
## cor
## -0.1676ggscatter(chl_summer, x = "recruit_est", y = "weighted_mean_chl",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean Chlorophyll",
title="Summer")
## Fall
lm_fall<-lm(weighted_mean_chl ~ recruit_est, data=chl_fall)
summary(lm_fall)##
## Call:
## lm(formula = weighted_mean_chl ~ recruit_est, data = chl_fall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.35212 -0.14826 -0.02091 0.12718 0.60037
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.66741015185 0.05146026998 12.969 <0.0000000000000002 ***
## recruit_est 0.00000003414 0.00000003186 1.071 0.288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2039 on 66 degrees of freedom
## Multiple R-squared: 0.0171, Adjusted R-squared: 0.002205
## F-statistic: 1.148 on 1 and 66 DF, p-value: 0.2879cor.test(chl_fall$recruit_est, chl_fall$weighted_mean_chl, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: chl_fall$recruit_est and chl_fall$weighted_mean_chl
## t = 1.0715, df = 66, p-value = 0.2879
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1111334 0.3580200
## sample estimates:
## cor
## 0.1307564ggscatter(chl_fall, x = "recruit_est", y = "weighted_mean_chl",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Fall Weighted Mean Chlorophyll",
title="Fall")
SST Fronts
# SST fronts
fprob<-read.csv(here::here('data/sst_fronts/fprob_seasonal_ts_gtf_3x3.csv')) #fprob across all individual strata
fprob.ind<-read.csv(here::here('data/sst_fronts/fprob_seasonal_ts_gtf_indv_substrata_3x3.csv')) #mean for each of 14 substrata
fprob.ns<-read.csv(here::here('data/sst_fronts/fprob_seasonal_ts_gtf_n_v_s_substrata_3x3.csv')) #mean for substrata N and S of Hudson canyon
# fprob with year lag
fprob.lag <- fprob %>%
mutate(fprob_lag1 = lag(weighted_mean_fprob,4))
# Join with recruit estimate
fprob.rec <- dplyr::full_join(recruit, fprob.lag %>%
group_by(year) %>%
filter(year %in% c(1998:2020)),
dplyr::select(year, season, recruit_est, mean_fprob, weighted_mean_fprob, fprob_lag1),
by = join_by(year))
fprob.rec <- fprob.rec[-c(1:29),] #removes year < 2000 (starting year for fprob)Maps (SST fronts)
SST Fronts analysis
## Across all strata
#by season
fprob_winter <- filter(fprob.rec, season %in% c("winter"))
#fprob_winter <- fprob_winter[-c(18),] # removes outlier
fprob_spring <- filter(fprob.rec, season %in% c("spring"))
#fprob_spring <- fprob_spring[-c(7),]
fprob_summer <- filter(fprob.rec, season %in% c("summer"))
#fprob_summer <- fprob_summer[-c(17),] #removes outlier
fprob_fall <- filter(fprob.rec, season %in% c("fall"))
#fprob_fall <- fprob_fall[-c(62),] #removes outlier
## One year lag - Summer (peak spawn)
lm_lag<-lm(fprob_lag1 ~ recruit_est, data=fprob_summer)
summary(lm_lag)##
## Call:
## lm(formula = fprob_lag1 ~ recruit_est, data = fprob_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07285 -0.02821 0.01042 0.03520 0.05229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.80741434188 0.01860718240 43.393 <0.0000000000000002 ***
## recruit_est -0.00000001501 0.00000001313 -1.144 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03817 on 18 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.06773, Adjusted R-squared: 0.01593
## F-statistic: 1.308 on 1 and 18 DF, p-value: 0.2678cor.test(fprob_summer$recruit_est, fprob_summer$fprob_lag1, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: fprob_summer$recruit_est and fprob_summer$fprob_lag1
## t = -1.1435, df = 18, p-value = 0.2678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6301882 0.2060033
## sample estimates:
## cor
## -0.2602413ggscatter(fprob_summer, x = "recruit_est", y = "fprob_lag1",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean SST Frontal Probability",
title="One Year Lag")
## Winter
lm_winter<-lm(weighted_mean_fprob ~ recruit_est, data=fprob_winter)
summary(lm_winter)##
## Call:
## lm(formula = weighted_mean_fprob ~ recruit_est, data = fprob_winter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089471 -0.042545 -0.007391 0.058791 0.095752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.780627375816 0.027197589952 28.702 <0.0000000000000002 ***
## recruit_est -0.000000004173 0.000000017864 -0.234 0.818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05943 on 19 degrees of freedom
## Multiple R-squared: 0.002863, Adjusted R-squared: -0.04962
## F-statistic: 0.05456 on 1 and 19 DF, p-value: 0.8178cor.test(fprob_winter$recruit_est, fprob_winter$weighted_mean_fprob, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: fprob_winter$recruit_est and fprob_winter$weighted_mean_fprob
## t = -0.23358, df = 19, p-value = 0.8178
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4742426 0.3871184
## sample estimates:
## cor
## -0.05351077ggscatter(fprob_winter, x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Winter Weighted Mean SST Frontal Probability",
title="Winter")
## Spring
lm_spring<-lm(weighted_mean_fprob ~ recruit_est, data=fprob_spring)
summary(lm_spring)##
## Call:
## lm(formula = weighted_mean_fprob ~ recruit_est, data = fprob_spring)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13127 -0.02817 0.01427 0.03149 0.09048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.73519304026 0.02774429480 26.499 <0.0000000000000002 ***
## recruit_est 0.00000003199 0.00000001822 1.755 0.0953 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06062 on 19 degrees of freedom
## Multiple R-squared: 0.1395, Adjusted R-squared: 0.09423
## F-statistic: 3.081 on 1 and 19 DF, p-value: 0.09535cor.test(fprob_spring$recruit_est, fprob_spring$weighted_mean_fprob, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: fprob_spring$recruit_est and fprob_spring$weighted_mean_fprob
## t = 1.7551, df = 19, p-value = 0.09535
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06935361 0.69339789
## sample estimates:
## cor
## 0.3735159ggscatter(fprob_spring, x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Spring Weighted Mean SST Frontal Probability",
title="Spring")
## Summer
lm_summer<-lm(weighted_mean_fprob ~ recruit_est, data=fprob_summer)
summary(lm_summer)##
## Call:
## lm(formula = weighted_mean_fprob ~ recruit_est, data = fprob_summer)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06348 -0.03465 0.01292 0.03491 0.05230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.788509842355 0.018862030048 41.804 <0.0000000000000002 ***
## recruit_est -0.000000002364 0.000000012389 -0.191 0.851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04121 on 19 degrees of freedom
## Multiple R-squared: 0.001913, Adjusted R-squared: -0.05062
## F-statistic: 0.03642 on 1 and 19 DF, p-value: 0.8507cor.test(fprob_summer$recruit_est, fprob_summer$weighted_mean_fprob, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: fprob_summer$recruit_est and fprob_summer$weighted_mean_fprob
## t = -0.19084, df = 19, p-value = 0.8507
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4666157 0.3954134
## sample estimates:
## cor
## -0.0437395ggscatter(fprob_summer, x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Summer Weighted Mean SST Frontal Probability",
title="Summer")
## Fall
lm_fall<-lm(weighted_mean_fprob ~ recruit_est, data=fprob_fall)
summary(lm_fall)##
## Call:
## lm(formula = weighted_mean_fprob ~ recruit_est, data = fprob_fall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071677 -0.022670 0.000026 0.026553 0.074282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.807176179801 0.019289269573 41.846 <0.0000000000000002 ***
## recruit_est -0.000000009798 0.000000012670 -0.773 0.449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04215 on 19 degrees of freedom
## Multiple R-squared: 0.03051, Adjusted R-squared: -0.02051
## F-statistic: 0.598 on 1 and 19 DF, p-value: 0.4489cor.test(fprob_fall$recruit_est, fprob_fall$weighted_mean_fprob, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: fprob_fall$recruit_est and fprob_fall$weighted_mean_fprob
## t = -0.7733, df = 19, p-value = 0.4489
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5638485 0.2779676
## sample estimates:
## cor
## -0.1746798ggscatter(fprob_fall, x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson"),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Fall Weighted Mean SST Frontal Probability",
title="Fall")
Fprob of each substrata
#join data
ind.rec <- dplyr::full_join(recruit, fprob.ind %>%
group_by(year) %>%
filter(year %in% c(1998:2020)),
dplyr::select(year, season, recruit_est, mean_fprob, weighted_mean_fprob, fprob_lag1),
by = join_by(year))
ind.rec <- ind.rec[-c(1:29),] #begin at year 2000
#correlation plot
ggscatter(ind.rec, x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~id) +
theme_facet()
# Winter
ggscatter(ind.rec %>%
filter(season == "winter"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~id) +
theme_facet()
# Spring
ggscatter(ind.rec %>%
filter(season == "spring"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~id) +
theme_facet()
# Summer
ggscatter(ind.rec %>%
filter(season == "summer"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~id) +
theme_facet()
# Fall
ggscatter(ind.rec %>%
filter(season == "fall"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~id) +
theme_facet()
Fprob N/S of Hudson Canyon
#join data
ns.rec <- dplyr::full_join(recruit, fprob.ns %>%
group_by(year) %>%
filter(year %in% c(1998:2020)),
dplyr::select(year, season, recruit_est, mean_fprob, weighted_mean_fprob, fprob_lag1),
by = join_by(year))
ns.rec <- ns.rec[-c(1:29),] #begin at year 2000
# Winter
ggscatter(ns.rec %>%
filter(season == "winter"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~substrat) +
theme_facet()
# Spring
ggscatter(ns.rec %>%
filter(season == "spring"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~substrat) +
theme_facet()
# Summer
ggscatter(ns.rec %>%
filter(season == "summer"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~substrat) +
theme_facet()
# Fall
ggscatter(ns.rec %>%
filter(season == "fall"),
x = "recruit_est", y = "weighted_mean_fprob",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method="pearson",
label.x=20, label.y=0.6, size =3),
add.params=list(color="dodgerblue", fill="lightgray"),
xlab = "Recruitment Estimate", ylab = "Mean SST frontal probability") +
facet_wrap(~substrat) +
theme_facet()
References:
Joyce, Terrence M, Young-Oh Kwon, Hyodae Seo, and Caroline C Ummenhofer. 2019. “Meridional Gulf Stream Shifts Can Influence Wintertime Variability in the North Atlantic Storm Track and Greenland Blocking.” Geophysical Research Letters 46 (3): 1702–8. https://doi.org/10.1029/2018GL081087.
Hobbs, L., Banas, N. S., Cottier, F. R., Berge, J., & Daase, M. (2020). Eat or sleep: availability of winter prey explains mid-winter and spring activity in an Arctic Calanus population. Frontiers in Marine Science, 7, 541564.
Pérez-Hernández, M. Dolores, and Terrence M. Joyce. 2014. “Two Modes of Gulf Stream Variability Revealed in the Last Two Decades of Satellite Altimeter Data.” Journal of Physical Oceanography 44 (1): 149–63. https://doi.org/10.1175/JPO-D-13-0136.1.